Methods and systems to determine the neural representation of digitally processed sounds

ABSTRACT

The present disclosure relates to methods for evaluating the sound quality of a digital engineering process by, in part, measuring the frequency following response (FFR) of the human auditory system elicited by identical auditory stimuli (e.g., a musical interval) encoded with variations of a digital signal processing technique (e.g., various sampling rates). Once measured, the FFR may be analyzed to determine the comparative effect of each digital signal processing technique on a human subject&#39;s ability to process complex stimuli presented by the digital engineering process.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is the U.S. national stage entry ofinternational application PCT/US2018/052870, filed Sep. 26, 2018, whichclaims the benefit of U.S. Provisional Application 62/563,999, filedSep. 27, 2017, the contents of which are incorporated herein byreference in their entireties.

TECHNICAL FIELD

Aspects of the present disclosure relate to digitally processed sound.

BACKGROUND

Audio engineering and audio device development generally involve thedevelopment of digital signal processing algorithms to generate,manipulate, and sonify signals for devices including headphones,speakers, hearing aids, and the like. Conventional methods forevaluating the efficacy of such digital signal processing techniquesrely on subjective ratings offered by one or more “golden eared”, humanlisteners. More specifically, in response to hearing multiple versionsof a sound, a “golden eared” listener provides a subjective rating ofthe quality of the sound. Evaluating sounds in such a manner is timeconsuming, labor-intensive, and error prone.

Accordingly, improved and objective evaluation methods are needed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, which are incorporated herein and constitutepart of this specification, illustrate non-limiting and non-exhaustiveembodiments of the present disclosure, and, together with thedescription provided herein, serve to explain various features of theinvention.

FIG. 1A depicts the spectrum of an auditory stimulus presented at asampling rate of 96 kHz, as described in the Examples herein. The twofundamental frequencies, 99 Hz (f₁) and 166 Hz (f₂), were created withequal intensity. The observed difference in intensity is attributed toinherent characteristics of the presentation system. FIG. 1B illustratesa Major 6^(th) chord, G2 and E3. The depicted musical interval used inthe Examples comprises the fundamental frequencies 99 and 166 Hz,corresponding to a Major 6^(th) chord, G2 and E3, respectively. FIG. 1Cdepicts a MATLAB-generated time domain waveform of the musical intervalused in the Examples.

FIG. 2 depicts a graph of a stimulus correlation analysis, includingthree test comparisons (22/44₁; 22/96; 44₁/96) and a control comparison(44₁/44₂), as described in the Examples herein. Although all comparisonsshow very high correlation, a decrease is noted for the two comparisonsinvolving the 22 kHz stimulus. Cohen's effect size calculationsconfirmed that there was a meaningful difference between the strength ofthe control-condition inter-stimulus correlations and the two conditionsinvolving the 22 kHz stimulus: 44₁/44₂ vs. 44₁/22 (d=1.13); and 44₁/44₂vs. 96/22 (d=1.55). The 44₁/96 comparison did not meaningfully differfrom the control (d=0.06).

FIG. 3 depicts a graph showing the grand average neural response spectra(n=12) for 22, 44, and 96 kHz conditions, as described in the Examplesherein. Neural responses to 4,000 repetitions of each chord, halfpresented with inverted polarity, were measured for each subject. Addingthe two response polarities (A₀B₀) emphasized natural distortions of thenonlinear auditory system. Subtracting the two response polarities(A₀B_(π)) emphasized the neural analogues of stimulus spectralcomponents, including fundamental frequencies (99 and 166 Hz) andharmonics. Neural response data above the noise floor was minimal forfrequencies greater than 1000 Hz.

FIG. 4 depicts a graph showing the grand average neural responsecorrelations for each possible sampling rate comparison, for bothsubtracted (A₀B_(π)) and added (A₀B₀) responses, as described in theExamples herein. Regarding the subtracted responses (A₀B_(π)), nosignificant difference was observed between any sampling ratecomparison, with all showing high correlation. Regarding the addedresponses (A₀B₀), significantly lower correlations were observed for thecomparisons involving the 22 kHz response (p=<0.001), relative to thecontrol comparison. The pattern of reduced inter-spectrum correlationsbetween 22 kHz and the two higher sampling rates is observed for ten oftwelve subjects (thin lines). There was no significant differencebetween the control 44₁/44₂ and 44₁/96 comparisons (p=0.21).

FIG. 5A (main graph) depicts a graph showing the grand average neuralresponse spectra (A₀B₀), as described in the Examples herein. Arrowsindicate regions of significant difference in spectral amplitude betweensampling rate conditions. These regions showed a frequency shift (>3 Hz)in 22 kHz response peaks, which, in turn, created significantdifferences in spectral amplitude for 22 kHz relative to 44 and 96 kHzconditions. In order of ascending frequency (Hz), peaks showing bothsignificant difference in 22 kHz amplitude as well as a frequency shiftinclude: 138.8 Hz (expected=132, 4SH), 468.4 (expected=463, f2+3f1),523.7 (expected=528, 16SH), 800.9 (not expected). FIG. 5B (inset) is agraph depicting examples of the observed frequency shift of FIG. 5A. Theshaded boxes mark the expected frequencies, +/−1 Hz. A shift was clearlyobserved at two additional peaks that did not meet significance criteriain spectral amplitude analysis.

FIG. 6 is an example computing environment, according to aspects of thepresent disclosure.

FIG. 7 is an example computing system or device, according to aspects ofthe present disclosure.

SUMMARY

Aspects of the present disclosure involve various systems and methodsfor determining how the brain distinguishes between variations of adigital sound, thereby indicating whether and to what extent digitalprocessing impacts how the human brain processes sounds. Morespecifically and in one non-limiting example, the disclosed systemincludes the application of a series of algorithms to determine theextent to which the human brain distinguishes between sounds presentedat three different sampling rates. A Frequency Following Response (FFR)of the human auditory brainstem may be used to record and analyze neuralactivity in response to auditory stimuli, such as digital signals.Measurable changes in the FFR are a more sensitive and reliable measureof the effect of digital engineering processes on the experience of alistener, as compared to conventional perceptual measurements of thehuman auditory system.

While the examples discussed herein relate to determining the extent towhich the human brain distinguishes between a sound presented at varioussampling rates, it is contemplated that any number of other digitalsignal processes could be applied, including filters, noise cancellationalgorithms, pitch altering algorithms, distortions, amplifications, andmore. Additionally, the present disclosure could apply to comparisons ofmultiple pieces of hardware, such as two or more pairs of headphones ortwo or more hearing aids.

In one aspect, the disclosed technology relates to a method fordeveloping an audio device, the method including: (a) presenting atleast two auditory stimuli to a subject, wherein each auditory stimulusis created by a process that uses different variations of the samedigital signal processing technique; (b) measuring the frequencyfollowing response (FFR) elicited by each stimulus and comparing themeasurements to identify any positive or negative effects in the FFRelicited by any one of the stimuli; and (c) developing an audio devicethat converts an analog auditory signal into a digital auditory signalusing the digital processing technique determined in step (b) to have amore positive effect or less negative effect on FFR. In one embodiment,an audio device is produced by the method. In another embodiment, theaudio device is an assistive listening device, a speaker, an earphone,or headphones. In another embodiment, the assistive listening deviceincludes a hearing aid or cochlear implant. In another embodiment, thedigital signal processing technique is a filter, a noise cancellationalgorithm, a noise reduction algorithm, a pitch-altering algorithm, acompression algorithm, a distortion, an amplification, or a variation insampling rate, bit depth, or bit rate. In another embodiment, measuringthe FFR includes measuring harmonics and/or distortion productsgenerated by the auditory system that are not present in the stimuli. Inanother embodiment, measurements of harmonics and/or distortion productspresent in the FFR but not present in the stimuli are compared toidentify any positive or negative effects in the FFR elicited by any oneof the stimuli. In another embodiment, the at least two auditory stimuliare generated from the same source.

In another aspect, the disclosed technology relates to a method forevaluating the sound quality of a digital engineering process,including: (a) presenting a first auditory stimulus to a subject,wherein the first auditory stimulus is created by a digital engineeringprocess that uses a first digital signal processing technique; (b)presenting a second auditory stimulus to a subject, wherein the secondauditory stimulus is created by the digital engineering process of step(a) but using a variation of the first digital signal processingtechnique; (c) measuring the frequency following response (FFR) elicitedby each of the first and second auditory stimuli, and comparing themeasurements to determine the effect of each stimulus on FFR; and (d)identifying the digital engineering process of step (a) or step (b)resulting in the more positive effect or less negative effect on FFR asthe process that provides superior sound quality. In one embodiment, thedigital signal processing technique is a filter, a noise cancellationalgorithm, a noise reduction algorithm, a pitch-altering algorithm, acompression algorithm, a distortion, an amplification, a variation insampling rate, bit depth, or bit rate. In another embodiment, thedigital signal processing technique is a sampling rate. In anotherembodiment, the sampling rate is in the range of 22 kHz to about 44 kHz.In another embodiment, the variation of the first digital signalprocessing technique is the absence of the first digital signalprocessing technique. In another embodiment, the first and secondauditory stimuli are generated from the same source.

In another aspect, the disclosed technology relates to an assistivelistening device, including a digital engineering process that includesa sampling rate of at least 22 kHz. In one embodiment, the sampling rateis not greater than about 44 kHz. In another embodiment, the deviceincludes a hearing aid or cochlear implant.

In another aspect, the disclosed technology relates to a system forevaluating the sound quality of a digital engineering process, thesystem including a computing device including at least one processorconfigured to perform a method for evaluating the sound quality of adigital engineering process. In one embodiment, the digital signalprocessing technique is a filter, a noise cancellation algorithm, anoise reduction algorithm, a pitch-altering algorithm, a compressionalgorithm, a distortion, an amplification, a sampling rate, a bit depth,or a bit rate. In another embodiment, the at least one processor isfurther configured to measure harmonics and/or distortion productsgenerated by the auditory system that are not present in the stimuli.

DETAILED DESCRIPTION

Analog-to-digital (AD) conversion of audio signals has become afundamental component of how listeners experience sound in the modernworld. An increasingly large percentage of auditory signals, includingspeech and music, are now experienced as electro-acousticallyregenerated, discrete-time digital representations of originalcontinuous-time analog signals. Consideration for each step in theanalog-to-digital transformation and the effect on listener perceptionand physiology is imperative for researchers and clinicians working inthe audio realm of the twenty-first century.

A fundamental component of AD conversion is sampling rate. Thedigitization of an analog signal requires that discrete time points beselected from a continuous waveform, a process known as sampling. Thenumber of times per second that an analog signal is sampled is thesampling rate, expressed in kHz. Faithful reproduction is governed bythe sampling theorem, or Nyquist-Shannon theorem, which states that allinformation within a continuous-time signal with a finite bandwidth andno frequency higher than W Hz can be faithfully represented as adiscrete sequence of samples, provided that the samples are spaced ½ Wseconds apart. This discovery provided the theoretical basis for auniversally accepted rule in the digitization of audio: to maintain fullfidelity, an analog signal must be sampled at a rate that is at leasttwice the highest frequency component contained within the signal. Theimpact of this theorem is reflected in the current sampling ratestandards for the production of electronic devices across multipleindustries, including manufacturers of music equipment and assistivedevices (e.g. hearing aids).

Commonly used sampling rates for the recording and transmission of audioinclude 8 kHz (telephone), 22.05 kHz (AM radio), 32 kHz (FM radio), 44.1kHz (compact-disc), 48 kHz (DVD audio), and 96-192 kHz (high-fidelitymusic). Digital hearing aids were developed using sampling rates rangingbetween 16 and 32 kHz, depending on both manufacturer and model. Reviewof literature provided by several current hearing aid manufacturers(Starkey, Oticon, Widex, Phonak) revealed that these rates have remainedstable in the current hearing aid market, typically attributed torestrictions imposed by power supply, device size, and transducerlimitations. The present disclosure focuses on sampling rates used inthe digitization of audio frequencies, not the much higher frequenciesthat are used as carriers in the transmission of audio signals, such asthe 2.4 GHz ISM band used in wireless transmission.

Considering the range of human hearing (up to about 20 kHz), 44.1 kHz isthe most commonly used sampling rate that is high enough to encode allaudible content of an analog signal. However, non-biological factorshave influenced the selection of sampling rates, including compatibilitywith other technology (e.g. DVD audio), efficient transmission of data(e.g. telephone, radio), power supply and size restrictions (e.g.hearing aids), and the desire for extremely high-fidelity sound in theentertainment industry.

The present disclosure provides methods to validate the effect ofvarious digital signal processing techniques on the conversion of ananalog auditory signal into a digital auditory signal. As used herein,the conversion of an analog auditory signal into a digital auditorysignal is referred to as “a digital engineering process.” The effect ofa digital engineering process on the experience of a listener can bemuch more reliably and sensitively measured based on quantifiablechanges in FFR, as described herein, as compared to previously usedsubjective measurements of perception.

In the context of the present disclosure, the concept of evaluating theeffect of a digital signal processing technique refers to determiningwhether the use of a first digital signal processing technique in adigital engineering process results in either a measurable positiveeffect or no measurable negative effect in the FFR of a listener ascompared to a digital engineering process that lacks the technique. Ifso, the digital engineering process that includes the first digitalsignal processing technique provides sound quality superior to that ofthe compared digital engineering process. This concept also includesdetermining whether a certain first variation of a first digital signalprocessing technique results in either a measurable positive effect orno measurable negative effect in the FFR of a listener as compared to adigital engineering process that uses a second different variation ofthe technique. If so, the digital engineering process that includes thefirst variation of the digital signal processing technique providessound quality superior to that of the compared digital engineeringprocess. Non-limiting examples of digital signal processing techniquesinclude use of a filter, a noise cancellation algorithm, a noisereduction algorithm, a pitch-altering algorithm, a compressionalgorithm, a distortion, an amplification, and variations in thesampling rate, bit depth, or bit rate.

In embodiments where the digital signal processing technique is the useof digital sampling, the variations in the technique may be variationsin the sampling rate. In various embodiments, the sampling rate may beat least about 22 kHz—for example, about 22 kHz, about 23 kHz, about 24kHz, etc. In further embodiments, the sampling rate may be at leastabout 25 kHz, at least about 30 kHz, at least about 35 kHz, or at leastabout 40 kHz. In still further embodiments, the sampling rate may beabout 35 kHz, about 36 kHz, about 37 kHz, about 38 kHz, about 39 kHz,about 40 kHz, about 41 kHz, about 42 kHz, about 43 kHz, or about 44 kHz.

Measuring the FFR is a non-invasive method of recording neural activityin response to auditory stimuli—e.g., an acoustic sound, such as acomplex sound. Non-limiting examples of suitable auditory stimuliinclude natural, synthetic, and hybrid complex sounds, such as musicalintervals, musical sounds, vocal sounds, environmental sounds, andcombinations thereof. In some embodiments, the auditory stimuli aregenerated from the same source. In one such instance, an initialacoustic sound may be generated and then processed by a single hardwaredevice using variations of a certain digital processing signaltechnique. In another such instance, an initial acoustic sound may begenerated and then processed by different hardware devices usingvariations of a certain digital processing signal technique.

While the FFR is primarily a subcortically-generated potential(Chandrasekaran & Kraus, 2010), its activity represents a confluence ofcognitive, sensorimotor, and reward networks (Kraus & White-Schwoch,2015). Evoked potentials elicited by numerous repetitions of an auditorystimulus are recorded using contact electrodes on the scalp. Responsesare then averaged to minimize variable background neural activity andisolate the relevant, invariant sound-evoked response. The FFR mirrorsthe spectral and temporal characteristics of the evoking stimulus withremarkable fidelity within the limits of the response bandwidth (Kraus,2011). Moreover, this measure captures the nonlinear aspects of auditoryprocessing, including harmonics and distortion products generated by theauditory system that are not present in the evoking stimulus.

FFR may be measured by presenting an acoustic stimulus comprising acomplex sound to a subject and recording the brain's response to theacoustic stimulus. Brain response can be recorded in a number ofdifferent ways. For example, the brain's response can be measured usingelectrodes that pick up electrical potentials generated by populationsof neurons in the brain of the subject—i.e., the electrodes measurevoltage potentials evoked by the acoustic stimulus presented to thesubject. The FFR measurement reflects sustained neural activity over apopulation of neural elements.

EXAMPLES

The following example is included to demonstrate various embodiments ofthe present disclosure. The use of this and other examples anywhere inthe specification is illustrative only, and in no way limits the scopeand meaning of the invention or of any exemplified form. Likewise, theinvention is not limited to any particular preferred embodimentsdescribed herein. Indeed, modifications and variations of the inventionmay be apparent to those skilled in the art upon reading thisspecification, and can be made without departing from its spirit andscope. The invention is therefore to be limited only by the terms of theclaims, along with the full scope of equivalents to which the claims areentitled.

Example 1

This example describes a study in which subjects were presented withacoustic signals comprising a complex sound—namely, a musical interval.The digital signal processing technique in this example is the use ofdiffering sampling rates, and measurements of FFR were used to validatethat different sampling rates have a significant effect on the neuralrepresentation of a musical interval. The use of the foregoing acousticstimulus is representative of acoustic stimuli that are suitable for usein connection with the present disclosure. Hence, other natural,synthetic, and/or a hybrid complex sounds may also be used. Likewise,the use of the foregoing digital processing techniques and measurementsof FFR are also representative of other digital processing techniquesand measurements of FFR that are suitable for use in connection with thepresent disclosure. See, for example, U.S. Patent Publication No.2016/0217267, hereby incorporated by reference in its entirety.

Experimental Method: Subjects. Twelve young adults (6 male, 6 female;age range 18-28) were recruited for participation in the study byword-of-mouth from the Evanston campus of Northwestern University. Allsubjects were monolingual English-speakers, had no history of otologicor neurologic dysfunction (self-report), and showed normal click-evokedABR latencies (Bio-logic Navigator Pro; Natus Medical Incorporated).Subjects were evaluated for normal peripheral auditory function on theday of testing through distortion-product otoacoustic emission (DPOAE)testing (Bio-logic Scout; Natus Medical Incorporated) and 226 Hztympanometry (easyTymp; MAICO Diagnostics) in each ear. All subjectsexhibited middle ear pressure and immittance within normal limits(Wiley, 1996) and exhibited present DPOAEs from 1 to 8 kHz, measuredusing accepted methodology and in reference to normative data (Martin etal., 1990).

Experimental Method: Stimulus Creation. Three stimulus conditions wereused in FFR measurement. The three stimuli differed only in samplingrate, including 22.05, 44.10, and 96.00 kHz, hereafter referred to as“22,” “44,” and “96,” respectively. These sampling rates were chosento 1) include values representative of the range of sampling rates inmodern media recording and reproduction devices, and 2) include a ratethat falls within the range used in assistive devices, i.e., 22.05 kHz.All stimuli were digitally created triangle-wave intervals (MATLABR2013a; The MathWorks, Inc.) consisting of equal-amplitude fundamentalfrequencies 99 and 166 Hz, corresponding to the musical interval of amajor sixth (E2 and G2) (FIG. 1 ). This frequency interval may beemployed with the use of a digital synthesizer. Expected responsefrequencies were established relative to fundamental frequencies (Lee etal., 2015). Each stimulus was 200 ms in length, with a 10 msHanning-function attack and release.

Experimental Method: Stimulus Presentation. Stimuli were presenteddiotically (identical, simultaneous presentation to both ears) throughER2 insert earphones (Etymotic Research) at an intensity of 70.5±0.2 dBsound pressure level (SPL) with an inter-stimulus interval of 85 ms(AUDCPT, Neuroscan Stim2 software; Compumedics). All three samplingrates were presented as randomly interleaved trials within a singlepresentation block, such that a total of 16,000 trials were presented.The test block consisted of 4000 trials each of the 22 and 96 kHzstimuli and 8000 trials of the 44 kHz stimulus. Doubling the 44 kHzcondition allowed for test-retest analysis of neural responses. For eachsampling rate condition, half of the trials were presented with invertedpolarity.

Experimental Method: Stimulus Analysis. Recordings of each stimulus weremade using an A-weighted sound level meter (Bruel & Kjaer, Type 2238Mediator) recording directly from the ear tube of the transducer (ER2insert earphone, Etymotic Research) with the same intensity andinter-stimulus interval used in presentation to subjects. The output ofthe sound level meter was recorded through the line-input of a MacBookPro (Apple) using LogicPro 9 recording software at a sampling rate of 96kHz and a bit depth of 24 bits.

Stimulus recordings included 400 repetitions of each sampling rate (22;44₁; 96) and a second block of the 44 kHz stimulus (44₂). Fast-Fouriertransform (FFT) yielded 1600 total corresponding stimulus spectra, eachwith a frequency resolution of 0.1 Hz. Inter-spectrum correlationanalysis was performed between each repetition of each condition forthree test comparisons (22/44₁; 22/96; 44₁/96) and a control comparison(44₁/44₂). Fisher transform of the resulting r-values yielded normallydistributed z′-scores for each individual repetition correlation.Pairwise Bonferroni-corrected t-tests and effect sizes were computedbetween comparisons.

Experimental Method: Neural Response Measurement. Measurement of the FFRwas conducted using a PC-based hardware/software EEG system (Scan 4.3Acquire, Neuroscan; Compumedics) and four Ag—AgCl scalp electrodesrecording differentially from the center vertex of the head (Cz, active)with linked earlobe references and a forehead ground. Contact impedancewas ≤5 kΩ for all electrodes and ≤2 kΩ between electrodes. Recordingsampling rate was 20 kHz. In order to monitor quality of incoming data,a filtered average (100-2000 Hz) was viewed during response recordingusing Scan 4.3 (Neuroscan Acquire software; Compumedics). Final averagesused in analysis were created offline (Matlab; procedure describedbelow) from a broadband (0.1-3500 Hz) response that was recordedsimultaneously. Due to the 1-2 kHz bandwidth limit of auditory midbrain(Liu et al., 2005; White Schwoch et al., 2016), a recording filter of0.1-3500 Hz and a 20 kHz recording sampling rate are appropriate.

For each subject, a click-evoked auditory brainstem response (ABR)measurement was conducted monaurally in each ear (3000 trials per ear)before and after the test block to 1) verify normal peripheral auditorysystem function, and 2) ensure reliability over the duration of the testblock, especially with respect to proper eartube insertion. Subjectswatched a muted movie of their choice with subtitles during testing. Allcomponents of the test protocol were performed for every subject.

Experimental Method: Neural Response Data Preparation. Average neuralresponse waveforms were created for each sampling rate condition (22;44₁; 44₂; 96 kHz) for each polarity (A; B) for each subject (n=12).Average waveforms were created for each subject from the first 2000non-artifact-rejected (>+/−35 uV) responses obtained for eachstimulus/polarity condition in a −40 to 245 ms time window, referencedto stimulus onset. Responses of opposing polarities were then added(A₀B₀) or subtracted (A₀B_(π)) to create two distinct types of responsewaveforms. Adding the two response polarities (A₀B₀) cancels thespectral components of the evoking stimulus, as well as the cochlearmicrophonic, and emphasizes the natural distortions of the nonlinearcentral auditory system. Subtracting the two response polarities(A₀B_(π)) cancels auditory system distortions and emphasizes the neuralanalogues of stimulus components, including fundamental frequencies andharmonics (Aiken & Picton, 2008). Importantly, the separation of thesetwo types of responses is not complete, as other components, such asdistortion products generated in the cochlea in response to two-tonestimuli, may be present in both A₀B₀ and A₀B_(π). Additionally, previoustone-evoked FFR studies have shown some overlap in the presence ofstimulus components and distortion products, including response peaks attwice the evoking frequency in the A₀B₀ response (Sohmer et al., 1977).

Response waveforms were Hanning-ramped and demeaned. A 200,000-point FFTwas performed on the 20-200 ms portion of each response waveform. Allsubsequent spectral analysis was performed within a 30-2000 Hzbandwidth, chosen to eliminate extraneous low-frequency noise andbecause most neural response data above 2000 Hz fell below the noisefloor. By excluding activity below 30 Hz and above 2000 Hz, we thereforeminimize the possibility that differences between responses are due tonon-auditory neural activity.

Experimental Method: Neural Response Analysis. Two methods were used toexamine a sampling rate effect between the measured neural responsespectra; inter-spectrum correlations and discrete peak amplitude andfrequency comparisons in the frequency domain. Time-domain waveformswere not used in any analysis.

Experimental Method: Neural Response Correlation Analysis.Inter-spectrum response correlations were performed on an individualsubject level for each of four possible comparisons (44₁/44₂; 22/44₁;44₁/96; 22/96). Response spectra obtained in the 44₁ condition were usedfor all test correlations involving the 44.1 kHz sample. Neuralresponses obtained in the 44₂ condition were used for control analysisonly.

Fisher transform of the resulting r-values yielded normally distributedz′-scores for each comparison for each subject to be used in subsequentstatistical analysis. Z′-scores were then averaged across subjects toyield a grand average z′-score for each comparison. Repeated-measuresANOVA was performed to determine the effect of sampling rate condition.

Experimental Method: Neural Response Peak Analysis. Individual peakswithin the response spectra (A₀B₀; A₀B_(π)) were analyzed todetermine 1) the response frequencies at which significant differencesin spectral amplitude occurred, and 2) the relationship of thesefrequencies to expected response peak frequencies. Expected responsefrequencies were established for the interval used in the current study(99 and 166 Hz) in a previous study (Lee et al., 2015), which took intoaccount both frequency components of the chord as well as distortionsproduced by the auditory system. Results for the A₀B₀ recordings areshown below in Table 1.

TABLE 1 Response Peak Analysis: A₀B₀ Observed Main Effect Peak FrequencyResponse Peak Relationship Expected Peak of Sampling 44₁/44₂ Shift for22 Frequency (Hz) to f₁, f₂ Frequency (Hz) Rate (p) 22/44₁ 22/96 44₁/96Control (% of subjects) 68 2SH, f₂ − f₁ 66, 67 130.7 3f₁ − f₂ 131 1324SH 132 138.8 — — 4.6⁻⁴ *** *** ✓ (83%) 193 — — 0.06 * * ✓ (92%) 196.52f₁, 6SH 198 199.9 2f₁, 6SH 198 207.5 — — 0.06 * * ✓ (83%) 331.7 2f₂,10SH 330, 332 397.5 [4f₁, 12SH], 396, 399 3f₁ − f₂ 464.1  f₂ + 3f₁ 463468.4 — — <1.0⁻⁸ *** *** ✓ (92%) 523.7 — — 0.015 *** ** ✓ (75%) 528.916SH, 2f₂ + 2f₁ 528, 530 0.09 * 594.5 6f₁, 18SH 594 663.3 4f₂ 664 726.522SH 726 795.9 24SH 792 800.9 — — <0.0001 *** *** ✓ (83%) 856.6 26SH 8580.03 ** *** 861.4 26SH 858 996.2 — — * p = <0.05 ** p = <0.01 *** p =<0.001

Table 1 includes individual response peak spectral amplitude analysisfor the A₀B₀ response. A significant main effect of sampling rateoccurred at 5 of 22 peaks (shaded rows). Pairwise comparisons revealedthat the effect was driven by a difference in spectral amplitude betweenthe 22 kHz response spectra and the other two sampling rates (22/44₁ and22/96). No significant difference in peak amplitude was observed in the44₁/96 or control (44₁/44₂) comparisons. A frequency shift, >3 Hz fromthe nearest expected peak frequency, occurred in the 22 kHz response at4 of the 5 peaks showing a significant main effect of sampling rate, aswell as 2 additional peaks that showed a trending effect, in a majorityof subjects (Table 1, column 9).

Expected response frequencies from Lee et al. (2015) included additionsand subtractions of the two fundamental frequencies and their harmonics,as well as the common subharmonic and its harmonics in the A₀B₀ response(Table 1, column 3).

Peaks of interest were chosen from the spectra obtained in this studyfollowing the simple criterion that at least one of the sampling rateconditions (22; 44₁; 96 kHz) showed a definable peak above the noisefloor, which included 10 peaks for A₀B_(π) and 22 peaks for A₀B₀ (column1 of Tables 1 and 2). Importantly, analysis was not limited to peaksidentified in Lee et al. (2015), and thus further included analysis of 7additional peaks for A₀B₀. Differences in spectral amplitude were thenanalyzed using repeated-measure ANOVA.

Results: Stimulus Analysis. Inter-spectrum correlation analysis of 400repetitions of each stimulus ultimately yielded a final average r-valueand z′-score for each comparison (22/44₁; 22/96; 44₁/96; 44₁/44₂). Veryhigh correlations were observed for all comparisons, with the highestcorrelation observed for the control comparison, 44₁/44₂ (r>0.9999,z′=4.96), and the 44₁/96 condition (r=0.9999, z′=4.94). A small decreasewas noted for the two comparisons involving the 22 kHz stimulus (44₁/22:r=0.9998, z′=4.74; and 96/22: r=0.9998, z′=4.66) (FIG. 2 ), a patternalso observed in A₀B₀ neural response correlations (FIG. 4 ).Bonferroni-corrected t-tests between the control condition's z′ scoresand the other z′-scores involving the control (e.g. 44₁/44₂ vs. 44₁/96)all were highly significant due to the extremely large number of samples(79,800) involved in this analysis. However, Cohen's effect sizecalculations confirmed that there was a meaningful difference betweenthe strength of the control-condition inter-stimulus correlations andthe two conditions involving the 22 kHz stimulus (44₁/44₂ vs. 44₁/22,d=1.13; 44₁/44₂ vs. 96/22, d=1.55). In contrast, the 44/96 comparisondid not meaningfully differ from the control (d=0.06).

Results: Neural Response Correlation Analysis. Neural response spectra(A₀B_(π); A₀B₀) for the three sampling rate conditions are illustratedin FIG. 3 . Correlation analysis of the grand average neural responsespectra showed a significant effect of sampling rate for the A₀B₀condition (FIG. 4 , right). The control comparison (44₁/44₂), asexpected, showed a very high correlation of the response spectra, r=0.95(z=1.77). Likewise, the spectrum correlation between the two highersampling rates (44₁/96) was also high (r=0.94, z′=1.74) and notsignificantly lower than the control condition (p=0.21). In contrast,the spectrum correlations that involved the 22 kHz condition, thoughstill strong, were significantly lower than the control (44₁/22: r=0.91,z′=1.50, p<0.001 and 96/22: r=0.91, z′=1.46, p<0.001). The pattern ofreduced inter-spectrum correlations between 22 kHz and the two highersampling rates is observed for ten of twelve subjects (FIG. 4 , thinlines). Pair wise comparisons were made using Bonferroni-correctedt-tests.

For the A₀B_(π) condition, there were no significant differences amongany of the response spectra, with all pairs showing high correlations(r=0.95−0.96; z′=1.8−1.9) (FIG. 4 , left).

Results: Neural Response Individual Peak Analysis, A₀B₀. Analysis ofindividual response peaks revealed a significant main effect of samplingrate on spectral amplitude at 5 of 22 peaks, with another threetrending. Pairwise comparisons revealed that the main effect was drivenby a difference in spectral amplitude for the 22 kHz response relativeto the 44 and 96 kHz responses (Table 1). It was discovered that at 4 ofthese 5 peaks, a peak frequency shift (>3 Hz from nearest expected) hadoccurred only in the 22 kHz response which, as a result, created thesignificant difference in spectral amplitude at the expected frequency(FIG. 5 ). A frequency shift (>3 Hz) was observed at 3 additional peaksthat showed only trending significance in spectral amplitude difference.Moreover, the frequency shifts were observed for the majority ofsubjects (Table 1, column 9).

In the control and 44/96 kHz response comparisons, no significantdifference in spectral amplitude or apparent frequency shift wasobserved at any response peak (Table 1, columns 6 and 7).

Results: Neural Response Individual Peak Analysis, A₀B_(π). No maineffect of sampling rate was observed at any response peak (see Table 2below).

TABLE 2 Response Peak Analysis: A₀B_(π) Response Peak RelationshipExpected Peak Main Effect of 44₁/44₂ Peak Frequency Frequency (Hz) tof₁, f₂ Frequency (Hz) Sampling Rate (Control) 22/44₁ 44₁/96 22/96 Shiftfor 22 30.2 2f₁ − f₂, SH 32, 33 99 f₁ 99 165.7 f₂ 166 233.2 2f₂ − f₁ 233297 3f₁, 9SH 297 361 11SH, 2f₁ + f₂ 363, 364 429 13SH, f₁ + 2f₂ 430, 431497.7 3f₂ 498 563 4f₂ + f₂ 562 830 5f₂ 830 * p = <0.05 ** p = <0.01 ***p = <0.001

Table 2 presents individual response peak spectral amplitude analysisfor the A₀B_(π) response. No main effect of sampling rate was observedat any response peak frequencies. No shift in peak frequency >3 Hz wasobserved at any peak.

Discussion: Through investigation with the frequency following response(FFR), it was determined that the auditory brain distinguishes stimuliencoded with different sampling rates. Spectral analysis of neuralresponses revealed that the lowest sampling rate condition, 22 kHz,differed significantly from 44 and 96 kHz conditions, which themselvesdid not differ from one another relative to a control comparison. Theresults suggest an effect of decreasing sampling rate as well as theexistence of a neural discrimination ceiling above which differences arenot observed. Ultimately, the results show that the auditory brain iscapable of distinguishing stimuli that differ only in the sampling ratewith which they were encoded.

Sampling rate effects on the stimuli were also considered. The similarcorrelational trends observed for stimuli and neural responses suggestthat differences observed in the 22 kHz response spectra were in factelicited by acoustic differences in the 22 kHz stimulus (FIG. 2 and FIG.4 ).

The sampling rate effect on the test stimuli was subtle. Sampling ratealone did not produce dramatic changes in stimulus amplitude orfrequency content with the use of a low-frequency-biased triangle wavestimulus. This was proven true in the results, as the correlationaleffect in the stimuli was present but small, such that determination ofthe exact differences in frequency content that were driving a decreasein 22 kHz correlations were difficult to realize in a meaningful way.Close inspection of the three spectra overlaid revealed that anyobservable spectral differences for 22 kHz were slight, were not moreprevalent in any specific frequency region, showed no apparent patternof increased or decreased spectral amplitude, and appeared no greaterthan those observed between 44 and 96 kHz. Importantly, the conspicuouspeak frequency shifts observed in A₀B₀ 22 kHz neural response were notpresent in the 22 kHz stimulus.

Despite unknowns in determining the exact mechanism underlying the 22kHz stimulus difference, inter-spectrum correlation analysis of numerousindividual repetitions yielded consistently lower correlations in allcomparisons that included the 22 kHz stimulus, supported by effect sizecalculations that identified a meaningful difference betweencomparisons. Considering neural response results, the stimulus analysisresults suggest the existence of a small stimulus sampling rate effectthat was nonetheless large enough to produce a significant effect in theauditory brain's processing of the stimuli.

Sampling rate effects on neural responses were evaluated. The ability ofthe auditory brain to distinguish sampling rate was shown throughspectral correlation analysis of the neural responses elicited by thethree test stimuli. The results indicate that the neural responseelicited by the 22 kHz stimulus differed significantly from thoseelicited by the 44 and 96 kHz stimuli, which in turn did not differ fromeach other. The subtle nature of the differences in the 22 kHz stimulus,as previously discussed, may underlie the unexpected result that aneural response sampling rate effect was observed only in the added,A₀B₀, response condition.

Auditory System Nonlinearities and the A₀B₀ Response. A sampling rateeffect was observed only in the neural response condition in which thetwo polarities were added (A₀B₀), a process that effectively cancels thespectral components of the evoking stimulus from the neural response.This method has been used to emphasize phase locking to the envelope ofthe stimulus and minimize the cochlear microphonic (Skoe & Kraus, 2010).For the current discussion, the most critical consequence of addingresponse polarities is the emphasis of natural distortions generated bynonlinearities of the auditory system (Aiken & Picton, 2008).

The neural response to a complex stimulus, such as the musical intervalused in this study, is not simply an analogue of stimuluscharacteristics. The acoustic interactions of the interval components(f₀ and harmonics) are processed in a nonlinear manner by the auditorysystem, generating neural frequencies that do not exist acoustically butappear in the spectra of the FFR. The generation of these additionalfrequency components, or distortion products (DPs), is complex, arisingas a result of first and possibly second-order frequency interactionsalong the auditory pathway. DPs can be generated at the level of thecochlea by nonlinearities in outer hair cell motion (Robles et al.,1991) or more centrally, as in the generation of envelope-related DPsand a common subharmonic in response to consonant intervals (Lee et al.,2015).

With this consideration, it was an unexpected and fascinating findingthat a sampling rate effect was observed only in the response conditionthat emphasized auditory system DPs (A₀B₀), and not in the responsecondition that emphasized spectral components of the stimulus (A₀B_(π)).With the use of far-field potential recordings, we cannot determinewhether the DPs observed in the A₀B₀ response reflect peripheral orcentral nonlinearities. However, the results suggest that differences inDP frequencies are driving the observed sampling rate effect.

Summarily, the results suggest that decreasing sampling rate has aneffect not on the mirror neural representation of a stimulus, but on theway in which nonlinearities of the auditory brain contribute to theevoked response.

Individual Response Peak Analysis: The 22 kHz Frequency Shift. Spectralamplitude analysis of individual response peaks led to an unexpecteddiscovery of shifts in the peak frequencies of the 22 kHz A₀B₀ response.A shift of greater than 3 Hz away from the mathematically expected peakwas observed at a total of 7 peaks, 4 of which also showed a significantdifference in spectral amplitude (with the remaining 2 trending). Onlyone expected peak (858 Hz) showed a significant difference in spectralamplitude without a frequency shift.

The influence of outliers was ruled out, as nearly identical shifts(degree and direction) were observed at the level of individual subjectsfor each peak (Table 1, column 9). However, between the peaks exhibitinga shift, there was no discernable pattern in the direction or degree ofthe shift: in some instances the shift was toward a lower frequency, inothers a higher frequency. In fact, several peaks exhibited a“multi-peaked” pattern in the 22 kHz response in regions wherewell-defined single peaks were present for 44 and 96 kHz conditions(FIG. 5 , inset a).

A Sampling Rate Effect Within a Limited Bandwidth. Increasing ordecreasing sampling rate affects the upper limit of the bandwidth thatcan be accurately encoded and subsequently reproduced, termed theNyquist frequency (NF). With higher sampling rates, the Nyquistfrequency increases and higher frequencies can be faithfully encoded.Interestingly, it is theoretically accepted that increasing samplingrate has no effect on lower frequency components, despite an increase insample points for those frequencies. In a relevant example, it would bepredicted that the increase in sampling rate from 22.05 kHz (NF: 11.025kHz) to 44.1 kHz (NF: 22.05 kHz) would have no acoustic effect on thesignal below 11.025 kHz.

Decades of intense and largely unresolved debate in the recordingcommunity, as well as the discrimination findings of Pras and Guastavino(2010), support the need for more controlled investigation of howsampling rate influences lower spectral components. Anecdotal claims ofthe ability to distinguish between 44.1 kHz and higher sampling rateswere supported by the controlled findings of Pras and Guastavino (2010).However, all listeners were limited by the same accepted bandwidth ofhuman sensitivity, 20-20,000 Hz, well below any additional frequencyinformation introduced with an increase from a 44.1 kHz sampling rate.Although the ultrasonic effect discovered by Oohashi et al. (2000) mayhave produced physiological changes in the subjects of Pras & Guastavino(2010), it is unlikely that it allowed for the observed discriminationability, as subjects in Oohashi et al. (2000) did not perceive thepresentation of ultrasonic frequencies.

These considerations led to a primary goal of this study; thedetermination of a sampling rate effect within a lower, limitedbandwidth. This design allowed for the additional benefit of ecologicalvalidity. Bandwidth limitations are ubiquitous in the world of digitalaudio, including those imposed by common audio reproduction devices(e.g. headphones, speakers), assistive devices (e.g. hearing aids), andultimately the limits of biological sensitivity (20-20,000 Hz).

The results discussed herein showed a measurable neural effect ofdecreasing sampling rate from 44 to 22 kHz within a bandwidth limited bythe use of low-frequency biased triangle-wave stimuli, with little to noacoustic content above the lowest Nyquist frequency, 11.025 kHz, in anyof the stimuli. In fact, little to no acoustic content was present inany stimulus beyond ˜7000 Hz. The FFR is even further bandwidth-limitedin that responses above ˜2000 Hz are rarely measurable. The discoverythat the effect was driven by frequency shifts in auditory system DPshints at a more subtle and complex mechanism underlying the effect ofessentially lower-fidelity AD conversion on listener physiology. If itwas desired to limit any frequency distortion effects in order tomaximize the experience of a listener, the results would support the useof a sampling rate higher than the minimum proposed by theNyquist-Shannon theorem for a given bandwidth.

Current Technologies: Hearing Aids. The effect of decreasing samplingrate to 22 kHz is less applicable to most modern recording andreproduction devices, as low-cost hardware interfaces and free recordingsoftware are now capable of sampling rates often exceeding 44.1 kHz. The22 kHz effect is more appropriately discussed in relation totechnologies that are still limited to lower sampling rates, perhapsmost importantly, hearing aids and other assistive listening devices.

The stage has been set for hearing aids to become more accessible andaffordable in the near future than ever before. At the national level,priorities for hearing healthcare were set forth by the NationalAcademies of Sciences, Engineering, and Medicine in a landmarkpublication focused on improving accessibility and affordability (NASEM,2016), followed within a year by the introduction of theOver-the-Counter Hearing Aid Act of 2017. In private industry, newplayers are entering the hearing aid market, from start-up developers ofpersonal sound amplification products (PSAPs) to technological giants. Acenterpiece of much discussion has been the technological specificationsthat will be required of hearing aid manufacturers as accessibilityimproves.

Improvements in digital hearing aid technology have included reducedsize, increased battery life, inclusion of a telecoil, feedbackreduction, and wireless connectivity (NASEM, 2016). Signal processinghas also improved, including development of new compression algorithmsand automatic noise reduction features based on real-time inputanalysis.

However, reproducible bandwidth has not shown a remarkable increase.Despite research showing a degradation in perceived sound quality whenupper cutoffs were reduced for speech (below 10,869 Hz) and music (below16, 854 Hz) (Moore & Tan, 2003), hearing aids rarely amplify sound above8,000 Hz, and there has been only recent development of devices thatclaim to reproduce up to 10,000 Hz. Transducer limitations are oftengiven as the cause of this bandwidth limit, but this obstacle seemsincreasingly unlikely given the rapid development of small,high-fidelity devices in other markets, and contradicts reports ofhearing aids with the capability of reproducing up to 16 kHz as long agoas the 1980's (Killion, 2009). It may also be considered that atraditional audiological assessment (125-8000 Hz) is used almostexclusively for programming and verification of appropriateamplification. Due to the current limits of hearing aid bandwidth andthe influence of the Nyquist-Shannon theorem, hearing aid manufacturershave not been obligated to increase sampling rate, as 16 kHz istheoretically sufficient to encode information up to 8000 Hz.

A Neural Discrimination Ceiling. Perhaps just as meaningful as an effectof decreasing sampling rate, albeit for a different population oflisteners, is the null finding for the 44/96 kHz comparison. Inconjunction with the observed 22 kHz effect, this null finding suggeststhe existence of a neural discrimination ceiling at a sampling ratefrequency between 22.05 and 44.1 kHz, above which the auditory braindoes not distinguish sampling rate.

The null result is a neural contradiction to the behavioral findings ofPras & Guastavino (2010), who showed that trained ears were able todiscriminate 44.1 and 88.2 kHz in an AB comparison task.

The results discussed herein show that decreasing sampling rate has asignificant effect on the neural representation of a musical interval,an effect driven by frequency shifts in the frequency following responseof the auditory brain to the lowest sampling rate condition, 22.05 kHz.This finding suggests that hearing aid users may especially benefit fromthe use of devices with sampling rates higher than current industrystandards. Additionally, this study is the first to objectively showthat the auditory brain does not benefit from an increase in samplingrate above a current music-industry standard, 44.1 kHz.

Systems

FIG. 6 illustrates a computing environment and/or computing system 800that automatically transmits acoustic stimuli, receives and processesbrain response data, and automatically determining how the braindistinguishes between variations of a digital sound, thereby indicatingwhether and to what extent digital processing impacts how the humanbrain processes sounds. More specifically, FIG. 6 illustrates acomputing environment and/or computing system 800 including a servercomputing device 808 operating in conjunction with various otherhardware and/or software components that may be used to perform orotherwise execute the various processes described herein.

Referring initially to FIG. 6 , the computing environment 800 includes atransducer controller 802 functionally coupled to an acoustic transducer804 and one or more electrodes 806. More specifically, the transducercontroller 802 represents a computing and/or processing device thatdelivers a stimulus to the acoustic transducer 804. Additionally, thetransducer controller 802 may receive and process brainwave signalinformation from the one or more electrodes 806. The transducercontroller 802 may be any suitable stimulus delivery and dataacquisition system, including PC-based stimulus delivery and dataacquisition systems such as those available from Bio-logic SystemsCorporation or Compumedics. The acoustic transducer 804 may be an insertearphone such as the ER-3 insert earphone available from EtymoticResearch, Elk Grove, Ill. The one or more electrodes 806 may be Ag—AgClscalp electrodes, which may be positioned on the test subject from Cz(active) to ipsilateral earlobe (reference) with forehead ground.

The transducer controller 802 may be functionally connected to acomputing device 808 including a memory 810 within which instructionsare retained directing the operation of the computing device 808 forcarrying out the herein described methods and processes. Morespecifically, the computing device 808 automatically generates a teststimulus signal, communicates the test stimulus signal to the transducercontroller 802 for generation of an acoustic stimulus that is presentedor otherwise provided to the test subject via the acoustic transducer804. The computing device 808 may obtain brain response data via theelectrodes 806 and the transducer controller 802. The brain responsedata may be stored within the memory 810 and/or stored or otherwisemaintained in a database 812.

The computing device 808 may transmit the brain response data to one ormore client devices 814-820. The one or more client devices 814-820functionally communicate with the computing device 808 through acommunications network 821, which may be the Internet, an intranet, anEthernet network, a wireline network, a wireless network, and/or anothercommunication network. The one or more client devices 814-820 may be apersonal computer, work station, mobile device, mobile phone, tabletdevice, processor, and/or other processing device capable ofimplementing and/or executing processes, software, applications, etc.,that includes network-enabled devices and/or software, such asuser-interface 818 for communication over the communications network 112(e.g., browsing the internet). Additionally, the one or more clientdevice(s) 814-820 may include one or more processors that processsoftware or other machine-readable instructions and may include a memoryto store the software or other machine-readable instructions and data.

The database 812 may include one or more data structures used to storeddata for analysis of the acquired brain response data. For example, thedatabase 812 may contain one or more data structures containingnormative response data to which the acquired brain response data may becompared to provide comparison data. The database 812 may furthercontain criteria data for evaluating the comparison data for determiningthe existence of a non-penetrating brain injury.

FIG. 7 illustrates an example of a suitable computing and networkingenvironment 900 that may be used to implement various aspects of thepresent disclosure. As illustrated, the computing and networkingenvironment 900 includes a general purpose computing device 900,although it is contemplated that the networking environment 900 mayinclude other computing systems, such as personal computers, servercomputers, hand-held or laptop devices, tablet devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronic devices, network PCs, minicomputers, mainframecomputers, digital signal processors, state machines, logic circuitries,distributed computing environments that include any of the abovecomputing systems or devices, and the like.

Components of the computer 900 may include various hardware components,such as a processing unit 902, a data storage 904 (e.g., a systemmemory), and a system bus 906 that couples various system components ofthe computer 900 to the processing unit 902. The system bus 906 may beany of several types of bus structures including a memory bus or memorycontroller, a peripheral bus, and a local bus using any of a variety ofbus architectures. For example, such architectures may include IndustryStandard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus,Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA)local bus, and Peripheral Component Interconnect (PCI) bus also known asMezzanine bus.

The computer 900 may further include a variety of computer-readablemedia 908 that includes removable/non-removable media andvolatile/nonvolatile media, but excludes transitory propagated signals.Computer-readable media 908 may also include computer storage media andcommunication media. Computer storage media includesremovable/non-removable media and volatile/nonvolatile media implementedin any method or technology for storage of information, such ascomputer-readable instructions, data structures, program modules orother data, such as RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium that may be used tostore the desired information/data and which may be accessed by thecomputer 900. Communication media includes computer-readableinstructions, data structures, program modules or other data in amodulated data signal such as a carrier wave or other transportmechanism and includes any information delivery media. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. For example, communication media may include wired mediasuch as a wired network or direct-wired connection and wireless mediasuch as acoustic, RF, infrared, and/or other wireless media, or somecombination thereof. Computer-readable media may be embodied as acomputer program product, such as software stored on computer storagemedia.

The data storage or system memory 904 includes computer storage media inthe form of volatile/nonvolatile memory such as read only memory (ROM)and random access memory (RAM). A basic input/output system (BIOS),containing the basic routines that help to transfer information betweenelements within the computer 900 (e.g., during start-up) is typicallystored in ROM. RAM typically contains data and/or program modules thatare immediately accessible to and/or presently being operated on byprocessing unit 902. For example, in one embodiment, data storage 904holds an operating system, application programs, and other programmodules and program data.

Data storage 904 may also include other removable/non-removable,volatile/nonvolatile computer storage media. For example, data storage904 may be: a hard disk drive that reads from or writes tonon-removable, nonvolatile magnetic media; a magnetic disk drive thatreads from or writes to a removable, nonvolatile magnetic disk; and/oran optical disk drive that reads from or writes to a removable,nonvolatile optical disk such as a CD-ROM or other optical media. Otherremovable/non-removable, volatile/nonvolatile computer storage media mayinclude magnetic tape cassettes, flash memory cards, digital versatiledisks, digital video tape, solid state RAM, solid state ROM, and thelike. The drives and their associated computer storage media, describedabove and illustrated in FIG. 7 , provide storage of computer-readableinstructions, data structures, program modules and other data for thecomputer 900.

A user may enter commands and information through a user interface 910or other input devices such as a tablet, electronic digitizer, amicrophone, keyboard, and/or pointing device, commonly referred to asmouse, trackball or touch pad. Other input devices may include ajoystick, game pad, satellite dish, scanner, or the like. Additionally,voice inputs, gesture inputs (e.g., via hands or fingers), or othernatural user interfaces may also be used with the appropriate inputdevices, such as a microphone, camera, tablet, touch pad, glove, orother sensor. These and other input devices are often connected to theprocessing unit 902 through a user interface 910 that is coupled to thesystem bus 906, but may be connected by other interface and busstructures, such as a parallel port, game port or a universal serial bus(USB). A monitor 912 or other type of display device is also connectedto the system bus 906 via an interface, such as a video interface. Themonitor 912 may also be integrated with a touch-screen panel or thelike.

The computer 900 may operate in a networked or cloud-computingenvironment using logical connections of a network interface or adapter914 to one or more remote devices, such as a remote computer. The remotecomputer may be a personal computer, a server, a router, a network PC, apeer device or other common network node, and typically includes many orall of the elements described above relative to the computer 900. Thelogical connections depicted in FIG. 7 include one or more local areanetworks (LAN) and one or more wide area networks (WAN), but may alsoinclude other networks. Such networking environments are commonplace inoffices, enterprise-wide computer networks, intranets and the Internet.

When used in a networked or cloud-computing environment, the computer900 may be connected to a public and/or private network through thenetwork interface or adapter 914. In such embodiments, a modem or othermeans for establishing communications over the network is connected tothe system bus 906 via the network interface or adapter 914 or otherappropriate mechanism. A wireless networking component including aninterface and antenna may be coupled through a suitable device such asan access point or peer computer to a network. In a networkedenvironment, program modules depicted relative to the computer 900, orportions thereof, may be stored in the remote memory storage device.

As explained in the various examples above, each step in theanalog-to-digital conversion pathway warrants systematic investigation,as each can have an effect on the experience of a listener. For example,decreasing sampling rate has a significant effect on the neuralrepresentation of a musical interval, an effect driven by frequencyshifts in the frequency following response of the auditory brain to thelowest sampling rate condition, 22.05 kHz. Such a finding suggests thathearing aid users may benefit from the use of devices with samplingrates higher than current industry standards. Additionally, theabove-study is the first to objectively show that the auditory braindoes not benefit from an increase in sampling rate above a currentmusic-industry standard, 44.1 kHz.

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We claim:
 1. A method for developing an audio device, the methodcomprising: (a) presenting at least two auditory stimuli to a subject,wherein each auditory stimulus is created by a process that usesdifferent variations of the same digital signal processing technique;(b) measuring the frequency following response (FFR) elicited by eachstimulus and comparing the measurements to identify any positive ornegative effects in the FFR elicited by any one of the stimuli; and (c)developing an audio device that converts an analog auditory signal intoa digital auditory signal using the digital processing techniquedetermined in step (b) to have a more positive effect or less negativeeffect on FFR.
 2. An audio device produced by the method of claim
 1. 3.The method of claim 1, wherein the audio device is an assistivelistening device, a speaker, an earphone, or headphones.
 4. The methodof claim 3, wherein the assistive listening device includes a hearingaid or cochlear implant.
 5. The method of claim 1, wherein the digitalsignal processing technique is a filter, a noise cancellation algorithm,a noise reduction algorithm, a pitch-altering algorithm, a compressionalgorithm, a distortion, an amplification, or a variation in samplingrate, bit depth, or bit rate.
 6. The method of claim 1, whereinmeasuring the FFR comprises measuring harmonics and/or distortionproducts generated by the auditory system that are not present in thestimuli.
 7. The method of claim 6, wherein measurements of harmonicsand/or distortion products present in the FFR but not present in thestimuli are compared to identify any positive or negative effects in theFFR elicited by any one of the stimuli.
 8. The method of claim 1,wherein the at least two auditory stimuli are generated from the samesource.
 9. A method for evaluating the sound quality of a digitalengineering process, comprising: (a) presenting a first auditorystimulus to a subject, wherein the first auditory stimulus is created bya digital engineering process that uses a first digital signalprocessing technique; (b) presenting a second auditory stimulus to asubject, wherein the second auditory stimulus is created by the digitalengineering process of step (a) but using a variation of the firstdigital signal processing technique; (c) measuring the frequencyfollowing response (FFR) elicited by each of the first and secondauditory stimuli, and comparing the measurements to determine the effectof each stimulus on FFR; and (d) identifying the digital engineeringprocess of step (a) or step (b) resulting in the more positive effect orless negative effect on FFR as the process that provides superior soundquality.
 10. The method of claim 9, wherein the digital signalprocessing technique is a filter, a noise cancellation algorithm, anoise reduction algorithm, a pitch-altering algorithm, a compressionalgorithm, a distortion, an amplification, a variation in sampling rate,bit depth, or bit rate.
 11. The method of claim 10, wherein the digitalsignal processing technique is a sampling rate.
 12. The method of claim11, wherein the sampling rate is in the range of 22 kHz to about 44 kHz.13. The method of claim 9, wherein the variation of the first digitalsignal processing technique is the absence of the first digital signalprocessing technique.
 14. The method of claim 9, wherein the first andsecond auditory stimuli are generated from the same source.
 15. A systemfor evaluating the sound quality of a digital engineering process, thesystem comprising a computing device comprising at least one processorconfigured to perform a method according to claim
 8. 16. The system ofclaim 15, wherein the digital signal processing technique is a filter, anoise cancellation algorithm, a noise reduction algorithm, apitch-altering algorithm, a compression algorithm, a distortion, anamplification, a sampling rate, a bit depth, or a bit rate.
 17. Thesystem of claim 15, wherein the at least one processor is furtherconfigured to measure harmonics and/or distortion products generated bythe auditory system that are not present in the stimuli.